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| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Context Management & Reliability | 15% | - Context handling
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
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NEW QUESTION # 141
A retail organization observes that Claude occasionally gives different wording when summarizing identical customer reviews. The summaries remain accurate. What is the MOST likely explanation?
Answer: D
Explanation:
Claude generates text probabilistically, meaning multiple valid outputs may exist for the same prompt. Small wording differences are expected even when factual content remains consistent.
Lower temperature can improve consistency if deterministic outputs are preferred.
NEW QUESTION # 142
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Testing reveals that when source documents are missing certain specifications, the model fabricates plausible- sounding values to satisfy your schema's required fields. For example, a document mentioning only dimensions receives a fabricated "weight: 2.3 kg" in the extraction output.
What schema design change most effectively addresses this hallucination behavior?
Answer: D
Explanation:
The schema is creating a structural incentive for fabrication. When a field is declared required, the output must contain a value even when the source document contains no corresponding evidence. Structured Outputs can guarantee that Claude's response conforms to a JSON Schema, but schema conformance does not establish that every generated value is factually supported. Anthropic's documentation shows that the required array determines which properties must be present; therefore, source-dependent properties that may legitimately be absent should not be included as required fields. ( https://platform.claude.com/docs/en/build- with-claude/structured-outputs ) Option B corrects the problem at the contract level. Claude can omit the unavailable property rather than inventing content merely to produce valid JSON. A nullable representation could also be used when downstream systems require a stable key set, but forcing an unsupported non-null value is architecturally unsound.
Option A still requires placeholder generation and does not resolve the mismatch between the schema and available evidence. Option C relies on model-generated confidence, which is not a substitute for grounding.
Option D is a useful secondary control, but it does not constitute the requested schema-design change.
Anthropic recommends allowing uncertainty and requiring factual claims to be grounded in the provided material. ( https://docs.anthropic.com/en/docs/test-and-evaluate/strengthen-guardrails/reduce-hallucinations ) Official references/topics: Structured Outputs-JSON Schema design; Reduce Hallucinations-allowing uncertainty and grounding claims.
NEW QUESTION # 143
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
1.5An engineer asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~6,000 lines total).
What's the most effective next step for building understanding while managing context constraints?
Answer: D
Explanation:
The correct objective is to construct an architectural map before consuming the full implementation.
Identifying the base cache abstraction, its interface, and the classes that implement or invoke it gives the agent a dependency-guided path through the code. It can then inspect only the invalidation implementations and integration points relevant to the proposed trigger.
This approach protects the context window. Anthropic states that every file read occupies context and that model performance can deteriorate as the window fills. Its Claude Code guidance warns against unbounded investigation that reads large numbers of files and recommends narrowing the exploration or delegating it. (
https://code.claude.com/docs/en/best-practices )
Option A is too lexical: searching only for invalidate or expire can miss event-driven invalidation, overridden methods, cache-key mutation, and generic interface calls. Option B loads approximately 6,000 lines without first establishing relevance. Option C assumes that filename patterns and file size correlate with architectural importance; the largest files may contain incidental code while a small interface defines the entire design.
Option D follows control and type relationships rather than arbitrary file order. After reading the base class, the agent can search for subclasses, imports, construction sites, middleware hooks, and calls to the invalidation contract, progressively expanding only where evidence requires it.
Official references/topics: Context-Efficient Exploration; Dependency-Guided Reading; Architectural Interfaces; Narrowly Scoped Investigation.
NEW QUESTION # 144
A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?
Answer: C
Explanation:
Explicit structural requirements reduce ambiguity. Providing exact column names, ordering, and formatting expectations helps Claude consistently generate Markdown tables that downstream users or systems can reliably consume.
NEW QUESTION # 145
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your pipeline reviews every pull request using a single API call with a static prompt containing the diff and the full text of each changed file. Unchanged files are not included. Developers report that reviews consistently miss cross-file bugs-for example, a pull request renames a function's parameters, but the review does not identify callers in unchanged files that still use the old argument order.
Evaluation shows that cross-file bugs account for 35% of production incidents originating from reviewed pull requests.
What is the most effective change to the review design?
Answer: D
Explanation:
Option C gives the reviewer access to the evidence currently missing from its prompt. An agentic review can use Grep, Glob, Read, language-server tools, and test commands to locate callers, follow imports, inspect type definitions, and verify whether a suspected compatibility issue actually exists. A turn limit controls cost while still allowing targeted exploration.
Anthropic's context-engineering guidance recommends just-in-time retrieval: agents should retain lightweight references and dynamically load the information needed for the current task instead of preloading a large fixed context. Anthropic has also reported that code-review performance improves when the necessary repositories are available for gathering complete context. Option A may load many irrelevant files while still missing reflection, generated interfaces, dependency injection, or indirect call paths beyond the arbitrary two- hop boundary. Option B cannot reason reliably about code that the model cannot inspect. Option D improves coverage but fragments repository-wide relationships and may duplicate substantial work. An agentic loop can begin with the diff, identify uncertain cross-file effects, search only relevant paths, and report findings only after locating supporting code. Anthropic context-engineering guidance , Claude Code quality report
NEW QUESTION # 146
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